Racialized Labour, Harassment, and Inequality in Canada’s Platformized Cultural Industries
Bibliographic record
Abstract
The ubiquity of social media use has facilitated the rise of a new cultural phenomenon: the “influencer” or content creator (Auxier & Anderson, 2021; Bishop & Duffy, 2023; Martineau, 2019). Content creators – a form of microcelebrity – accumulate and entertain their subscribers by posting textual and visual content (Abidin, 2021; Turner & Hui, 2023), and use their “visibility” – measured as the number of followers – to monetize their social media presence through partnerships with brands or the development of their own products (Harris et al., 2023; Reinikainen et al., 2020). Although a relatively recent phenomenon, early reports show that the top global influencers are overwhelmingly white (Gillespie, 2020; Hund, 2023). Research is emerging as to how this racial inequality is produced in social media platforms. In a Canadian context, there is a clear gap in the existing research about the demographics of Canadian influencers, as well as how racialized influencers experience online visibility, and what forms of inequality they may face due to online racism. Therefore, in this doctoral project, I seek to expand on one broad objective: to understand the scope, form and experience of racial inequality among content creators working in social media platforms in the Canadian context by looking at labour practices, environment and specific experiences of discrimination and harassment. By using mixed methodologies, including social media hashtag analysis, survey and interview data, firstly I show how Canadian creators’ communicative strategies are relatively homogenous and tend to reproduce neoliberal rationalities of individualism and commodification, which I argue calls into question the ability of creators’ to change and challenge the form of their labour. Secondly, I demonstrate how online harassment is a widespread workplace hazard for content creators, regardless of identity; however, the consequences of this harassment are qualitatively different for those who have been historically marginalized. Finally, I argue that racism presents in multilateral, dynamic, and simultaneous ways, which compounds negative material and epistemic outcomes for racialized creators in Canada. Thus, this thesis argues that within platformized labour, racism is reproduced in pervasive and subtle socio-technical ways which has consequences of contributing to differential outcomes for racialized creators.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.031 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".